Template for contribution to Computo
Example based on the Rmarkdown system
Computo team
2021-04-15
Introduction
About this document
This document provides a Rmarkdown1 template for contributions to the Computo Journal. We show how R or Python code can be included.
Advice in writting your manuscript
First make sure that you are able to build your manuscript as a regular notebook on your system. Then you can start configure the binder environment.
Formating the notebook
This section is about writing a notebook with the Rmarkdown system, typically for R users.
Rmarkdown basics
We first quickly cover the most basic features of Rmarkdown, that is, formatting text with markdown, math with \(\LaTeX\) via MathJax and bibliographical references via \(Bib\TeX\).
Rmarkdown is a simple formatting system for authoring HTML and PDF documents, that relies on the markdown markup language.
To render the document as HTML within Rstudio, click the Knit button. A document will be generated that includes both content as well as the output of any embedded R code chunks within the document. Alternatively, the shortcut Ctrl + Maj + K will produces the same result.
Mathematical formulae
\(\LaTeX\) code is natively supported, which makes it possible to use mathematical formulae:
\[ f(x_1, \dots, x_n; \mu, \sigma^2) = \frac{1}{\sigma \sqrt{2\pi}} \exp{\left(- \frac{1}{2\sigma^2}\sum_{i=1}^n(x_i - \mu)^2\right)} \]
References
References are displayed as footnotes using bibtex, e.g. [@computo] will display as (Computo Team 2020), where computo is the bibtex key for this entry. The bibliographic information is automatically retrieved from the .bib file specified in the header of this document (here: template-computo-Rmarkdown.bib).
R code
R code (R Core Team 2020) chunks may be embedded as follows:
knitr::kable(summary(cars))| speed | dist | |
|---|---|---|
| Min. : 4.0 | Min. : 2.00 | |
| 1st Qu.:12.0 | 1st Qu.: 26.00 | |
| Median :15.0 | Median : 36.00 | |
| Mean :15.4 | Mean : 42.98 | |
| 3rd Qu.:19.0 | 3rd Qu.: 56.00 | |
| Max. :25.0 | Max. :120.00 |
Including Plots
Plots can be generated as:
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.
Interative plots may also be produced in the HTML output of the document:
library("plotly")
ggplotly(p)Python Code
The R package reticulate (Ushey, Allaire, and Tang 2020) includes a Python engine for R Markdown that enables easy interoperability between Python and R chunks. Below we demonstrate a small subset of the available functionalities. We refer to the vignette R Markdown Python Engine for a more detailed description.
Setup
library("reticulate")
use_virtualenv("computo-template")First make sure (here, in R) that the required python modules are available
if (!py_module_available("seaborn")) py_install("seaborn")
if (!py_module_available("pandas")) py_install("pandas")
if (!py_module_available("matplotlib")) py_install("matplotlib")Using python
Example of python code and associated output:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="whitegrid", palette="muted")
# Load the example iris dataset
iris = sns.load_dataset("iris")
# "Melt" the dataset to "long-form" or "tidy" representation
iris = pd.melt(iris, "species", var_name="measurement")
plt.figure()
# Draw a categorical scatterplot to show each observation
sns.swarmplot(x="measurement", y="value", hue="species", palette=["r", "c", "y"], data=iris)
plt.show()
Communication between R and python chunks
All objects created within Python chunks are available to R using the py object exported by the reticulate package, e.g.:
rmarkdown::paged_table(py$iris)Conversely, all objects created within R are available from Python using the r object exported by the reticulate:
First, let us create an object within R:
data(volcano)rmarkdown::paged_table(as.data.frame(volcano))This object is accessible from Python:
print(r.volcano)## [[100. 100. 101. ... 104. 104. 103.]
## [101. 101. 102. ... 105. 104. 104.]
## [102. 102. 103. ... 105. 105. 104.]
## ...
## [ 98. 98. 98. ... 94. 94. 94.]
## [ 97. 98. 98. ... 94. 94. 94.]
## [ 97. 97. 97. ... 94. 94. 94.]]Other languages
Theoretically, you can include many others languages into Rmarkdown including Julia and C++. If you are comfortable enough to configure binder and prove us the reproducibility of your code, feel free to use any other language.
Session information
sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS/LAPACK: /usr/local/miniconda/envs/computorbuild/lib/libopenblasp-r0.3.12.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] reticulate_1.18 plotly_4.9.3 ggplot2_3.3.3
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.6 pillar_1.6.0 compiler_4.0.3 highr_0.8
## [5] tools_4.0.3 digest_0.6.27 viridisLite_0.4.0 jsonlite_1.7.2
## [9] lattice_0.20-41 evaluate_0.14 lifecycle_1.0.0 tibble_3.1.0
## [13] gtable_0.3.0 nlme_3.1-152 mgcv_1.8-34 pkgconfig_2.0.3
## [17] rlang_0.4.10 Matrix_1.3-2 DBI_1.1.1 crosstalk_1.1.1
## [21] yaml_2.2.1 xfun_0.20 httr_1.4.2 withr_2.4.1
## [25] stringr_1.4.0 dplyr_1.0.5 knitr_1.31 htmlwidgets_1.5.3
## [29] generics_0.1.0 vctrs_0.3.7 grid_4.0.3 tidyselect_1.1.0
## [33] data.table_1.14.0 glue_1.4.2 R6_2.5.0 fansi_0.4.2
## [37] rmarkdown_2.7 tidyr_1.1.3 farver_2.1.0 purrr_0.3.4
## [41] magrittr_2.0.1 scales_1.1.1 ellipsis_0.3.1 htmltools_0.5.1.1
## [45] splines_4.0.3 assertthat_0.2.1 colorspace_2.0-0 labeling_0.4.2
## [49] utf8_1.2.1 stringi_1.5.3 lazyeval_0.2.2 munsell_0.5.0
## [53] crayon_1.4.1